Exploring the therapeutic mechanisms of resveratrol for treating arecoline-induced malignant transformation in oral epithelial cells: insights into hub targets.

BACKGROUND: Betel nut chewing is a significant risk factor for oral cancer due to arecoline, its primary active component. Resveratrol, a non-flavonoid polyphenol, possesses anti-cancer properties. It has been shown to inhibit arecoline-induced oral malignant cells in preliminary experiments but the underlying mechanism remains unclear. This research therefore aimed to explore the potential therapeutic targets of resveratrol in treating arecoline-induced oral cancer. METHODS: Data mining identified common targets and hub targets of resveratrol in arecoline-induced oral cancer. Gene set variation analysis (GSVA) was used to score and validate the expression and clinical significance of these hub targets in head and neck cancer (HNC) tissues. Molecular docking analysis was conducted on the hub targets. The effect of resveratrol intervention on hub targets was verified by experiments. RESULTS: Sixty-one common targets and 15 hub targets were identified. Hub targets were highly expressed in HNC and were associated with unfavorable prognoses. They played a role in HNC metastasis, epithelial-mesenchymal transition, and invasion. Their expression also affected immune cell infiltration and correlated negatively with sensitivity to chemotherapeutic agents such as bleomycin and docetaxel. Experiments demonstrated that resveratrol down-regulated the expression of the hub targets, inhibited their proliferation and invasion, and induced apoptosis. CONCLUSION: Resveratrol inhibits the arecoline-induced malignant phenotype of oral epithelial cells by regulating the expression of some target genes, suggesting that resveratrol may be used not only as an adjuvant treatment for oral cancer, but also as an adjuvant for oral cancer prevention due to its low toxicity and high efficacy. © 2024 Society of Chemical Industry.

Abundance of reactive oxygen species (ROS) is associated with tumor aggressiveness, immune response, and worse survival in breast cancer.

PURPOSE: Reactive oxygen species (ROS) are oxygen-containing molecules that have high reactivity and play roles in protection or harm the cancer cells. We aimed to clarify the clinical relevance of ROS in breast cancer (BC) tumor microenvironment (TME). We hypothesized that it is associated with worse BC patient outcomes. METHODS: ROS score was generated by Gene Set Variation Analysis of Hallmark ROS pathway gene set and a total of 6245 BC patients were analyzed. RESULTS: High ROS BC significantly enriched cell proliferation-related gene sets (MYC targets v1 and v2, G2M checkpoint, E2F targets), pro-cancer-related gene sets (DNA repair, unfolded protein response, MTORC1 signaling, PI3K/AKT/MTOR signaling, glycolysis, and oxidative phosphorylation), immune-related gene sets (inflammatory response, allograft rejection, interferon-α and γ responses, complement, and IL6/JAK/STAT3 signaling), and infiltrated immune cells (CD4+ memory and CD8+ T cells, Th1 and Th2, dendritic cells, Tregs, M1 and M2 macrophages) and B cells, as well as elevated cytolytic activity consistently in both METABRIC and GSE96058 cohorts. Cancer cells were the major source of ROS in BC TME of single-cell sequence (GSE75688) cohort. High ROS was associated with intratumor heterogeneity, homologous recombination defects, mutation rates, and neoantigens, and with clinical aggressiveness in AJCC stage, Nottingham grade and Ki67 expression, as well as worse overall survival in both GSE96058 and METABRIC, and with worse disease-specific survival in METABRIC. CONCLUSION: Abundant ROS in BC patients is associated with abundant mutations, aggressive cancer biology, immune response, and worse survival.

Identification of an at-risk subpopulation with high immune infiltration based on the peroxisome pathway and TIM3 in colorectal cancer.

BACKGROUND: Peroxisomes are pivotal metabolic organelles that exist in almost all eukaryote cells. A reduction in numbers and enzymatic activities of peroxisomes was found in colon adenocarcinomas. However, the role of peroxisomes or the peroxisome pathway in colorectal cancer (CRC) is not defined. METHODS: In the current study, a peroxisome score was calculated to indicate the activity of the peroxisome pathway using gene set variant analysis based on transcriptomic datasets. CIBERSORTx was chosen to infer enriched immune cells for tumors among subgroups. The SubMap algorithm was applied to predict its sensitivity to immunotherapy. RESULTS: The patients with a relatively low peroxisome score and high level of T-cell immunoglobulin and mucin domain 3 (TIM-3) presented the worse overall survival than others. Moreover, low peroxisome scores were associated with high infiltration of lymphocytes and poor prognosis in those CRC patients. Thus, a PERLowTIM3High CRC risk subpopulation was identified and characterized by high immune infiltration. The results also showed that CD8 T cells and macrophages highly infiltrated tumors of the PERLowTIM3High group, regardless of consortium molecular subtype and microsatellite instability status. This subgroup had the highest tumor mutational burden and overexpression of immune checkpoint genes. Further, the PERLowTIM3High group showed a higher probability of responding to programmed cell death protein-1-based immunotherapy. In addition, genes involved in peroxisomal metabolic processes in CRC were also investigated since peroxisome is a rather pleiotropic and highly metabolic organelle in cell. The results indicated that only those genes involved in fatty acid alpha oxidation could be used to stratify CRC patients as similar as peroxisome pathway genes. CONCLUSIONS: We revealed the favorable prognostic value of the peroxisome pathway in CRC and provided a new CRC stratification based on peroxisomes and TIM3, which might be helpful for CRC diagnostics and personalized treatment.

Arylsulfatase I is a prognostic biomarker for head and neck squamous cell carcinoma and Pan-cancer.

BACKGROUND: Sulfatase gene family members mediate various biological functions in tumor stroma and tumor cell environments. However, the expressions and prognostic value of Arylsulfatase I (ARSI), a sulfatase gene family member, in head and neck squamous cell carcinoma (HNSC) have not been fully established. METHODS: Arylsulfatase I expressions in pan-cancer were profiled using publicly available databases. Then, univariate Cox regression, Kaplan-Meier, and the Pearson's correlation analyses were performed to determine correlations between ARSI expressions and cancer prognosis, immune cell status, and drug sensitivity. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to assess the potential mechanisms underlying ARSI functions in HNSC. RESULTS: Arylsulfatase I was highly expressed in 15 cancer types, with significant expressions in HNSC. Elevated ARSI levels were associated with worse prognostic outcomes in HNSC patients. In addition, GSVA and GSEA showed that ARSI was highly involved in tumor cell escape and inflammatory responses. Expressions of ARSI negatively correlated with tumor mutation burden or microsatellite instability and positively correlated with immune-related genes. Elevated ARSI expressions conferred poor tolerance to daporinad and sinularin, but increased cell sensitivity to dasatinib and XAV939. CONCLUSION: Arylsulfatase I is a promising prognostic and therapeutic target for HNSC.

A revised formulation of the generalized subsystem vibrational analysis (GSVA).

In this work, a simplified formulation of our recently developed generalized subsystem vibrational analysis (GSVA) for obtaining intrinsic fragmental vibrations (J Chem Theory Comput 14:2558, 2018) is presented. In contrast to the earlier implementation, which requires the explicit definition of a non-redundant set of internal coordinate parameters to be constructed for the subsystem, the new implementation circumvents this process by employing massless Eckart conditions to the subsystem fragment paired with a Gram-Schmidt orthogonalization to span the same internal vibration space indirectly. This revised version of GSVA (rev-GSVA) can be applied to equilibrium structure as well as transition state structure, and it has been incorporated into the open-source package UniMoVib (https://github.com/zorkzou/UniMoVib). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00214-021-02727-y.

High Expression of miR-34a Associated with Less Aggressive Cancer Biology but Not with Survival in Breast Cancer.

Most breast cancer (BC) patients succumb to metastatic disease. MiR-34a is a well-known tumor suppressive microRNA which exerts its anti-cancer functions by playing a role in p53, apoptosis induction, and epithelial-mesenchymal transition (EMT) suppression. Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) cohorts were used to test our hypothesis that miR-34a high BCs translate to less aggressive cancer biology and better survival in large cohorts. There was no association between miR-34a expression levels and clinicopathological features of BC patients except for HER2 positivity. MiR-34a high expressing tumors were associated with lower Nottingham pathological grades and lower MKI67 expression. In agreement, high miR-34a tumors demonstrated lower GSVA scores of cell cycle and cell proliferation-related gene sets. High miR-34a tumors enriched the p53 pathway and apoptosis gene sets. Unexpectedly, high miR-34a tumors also associated with elevated EMT pathway score and ZEB1 and two expressions. MiR-34a expression did not associate with any distant metastasis. Further, high miR-34a tumors did not associate with better survival compared with miR-34a low tumors. In conclusion, the clinical relevance of miR-34a high expressing tumors was associated with suppressed cell proliferation, enhanced p53 pathway and apoptosis, but enhanced EMT and these findings did not reflect better survival outcomes in large BC patient cohorts.

Gastric cancer with enhanced myogenesis is associated with less cell proliferation, enriched epithelial-to-mesenchymal transition and angiogenesis, and poor clinical outcomes.

Gastric cancer (GC) remains a lethal disease, with over 26,000 new cases and more than 11,000 deaths annually in the US. Thus, a deeper understanding of GC biology is critical to improve survival. Myogenesis is the formation of muscle fibers, which is a mesodermal tissue. In cancer, epithelial-to-mesenchymal transition (EMT) is a known phenomenon that promotes metastasis and poor survival. Given that myogenesis produces mesenchymal cells, we hypothesized that GC with increased myogenesis is linked to aggressive tumor behaviors and less favorable outcomes. In this study, three GC patient cohorts: TCGA (n=375), GSE26253 (n=432), and GSE84437 (n=482), were analyzed. The "MYOGENESIS" set in the Hallmark collection which comprises 200 myogenesis-related genes was analyzed to perform gene set variation analysis to create a score to quantify the myogenesis activity. Our results showed that T category of AJCC cancer staging that reflects the tumor invasion to stomach wall consistently correlated with myogenesis activity in two GC cohorts. High myogenesis GC was associated with lower cell proliferation, evidenced by reduced proliferation scores, decreased Ki67 gene expression, and less enrichment of E2F Targets, G2M checkpoint, MYC Targets V1, and V2 gene sets. High myogenesis tumors showed increased stromal cells (fibroblasts and adipocytes) infiltration within the tumor microenvironment, as well as less silent and non-silent mutation rates and copy number alterations. Higher lymphocyte infiltration, leukocyte fraction, T-cell receptor richness, and B-cell receptor richness were associated with high myogenesis GC. However, infiltration of CD4 cells, T helper type 1 and 2 cells, Natural Killer cells, regulatory T cells, and plasma cells was lower, with increased infiltration of dendritic cells in high myogenesis GC. High myogenesis GC enriched EMT, Hedgehog, TGF-ß, and KRAS gene sets. Furthermore, it was associated with enhanced angiogenesis, evidenced by enrichment of Angiogenesis, Coagulation, and Hypoxia gene sets, and increased infiltration of microvascular and lymphatic endothelial cells and pericytes. High myogenesis GC consistently correlated with worse overall survival in all three cohorts, and worse disease-specific and progression-free survival in the TCGA cohort. Hence, our findings suggest that GC with enhanced myogenesis is associated with decreased cell proliferation, increased EMT and angiogenesis, and worse prognosis.

Evaluating the Role of Neddylation Modifications in Kidney Renal Clear Cell Carcinoma: An Integrated Approach Using Bioinformatics, MLN4924 Dosing Experiments, and RNA Sequencing.

BACKGROUND: Neddylation, a post-translational modification process, plays a crucial role in various human neoplasms. However, its connection with kidney renal clear cell carcinoma (KIRC) remains under-researched. METHODS: We validated the Gene Set Cancer Analysis Lite (GSCALite) platform against The Cancer Genome Atlas (TCGA) database, analyzing 33 cancer types and their link with 17 neddylation-related genes. This included examining copy number variations (CNVs), single nucleotide variations (SNVs), mRNA expression, cellular pathway involvement, and methylation. Using Gene Set Variation Analysis (GSVA), we categorized these genes into three clusters and examined their impact on KIRC patient prognosis, drug responses, immune infiltration, and oncogenic pathways. Afterward, our objective is to identify genes that exhibit overexpression in KIRC and are associated with an adverse prognosis. After pinpointing the specific target gene, we used the specific inhibitor MLN4924 to inhibit the neddylation pathway to conduct RNA sequencing and related in vitro experiments to verify and study the specificity and potential mechanisms related to the target. This approach is geared towards enhancing our understanding of the prognostic importance of neddylation modification in KIRC. RESULTS: We identified significant CNV, SNV, and methylation events in neddylation-related genes across various cancers, with notably higher expression levels observed in KIRC. Cluster analysis revealed a potential trade-off in the interactions among neddylation-related genes, where both high and low levels of gene expression are linked to adverse prognoses. This association is particularly pronounced concerning lymph node involvement, T stage classification, and Fustat score. Simultaneously, our research discovered that PSMB10 exhibits overexpression in KIRC when compared to normal tissues, negatively impacting patient prognosis. Through RNA sequencing and in vitro assays, we confirmed that the inhibition of neddylation modification could play a role in the regulation of various signaling pathways, thereby influencing the prognosis of KIRC. Moreover, our results underscore PSMB10 as a viable target for therapeutic intervention in KIRC, opening up novel pathways for the development of targeted treatment strategies. CONCLUSION: This study underscores the regulatory function and potential mechanism of neddylation modification on the phenotype of KIRC, identifying PSMB10 as a key regulatory target with a significant role in influencing the prognosis of KIRC.

Developing and validating the model of tumor-infiltrating immune cell to predict survival in patients receiving radiation therapy for head and neck squamous cell carcinoma.

Background: Radiotherapy (RT) is a mainstay of head and neck squamous cell carcinoma (HNSCC) treatment. Due to the influence of RT on tumor cells and immune/stromal cells in microenvironment, some studies suggest that immunologic landscape could shape treatment response. To better predict the survival based on genomic data, we developed a prognostic model using tumor-infiltrating immune cell (TIIC) signature to predict survival in patients undergoing RT for HNSCC. Methods: Gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Data from HNSCC patients undergoing RT were extracted for analysis. TIICs prevalence in HNSCC patients was quantified by gene set variation analysis (GSVA) algorithm. TIICs and post-RT survival were analyzed using univariate Cox regression analysis and used to construct and validate a tumor-infiltrating cells score (TICS). Results: Five of 26 immune cells were significantly associated with HNSCC prognosis in the training cohort (all P<0.05). Kaplan-Meier (KM) survival curves showed that patients in the high TICS group had better survival outcomes (log-rank test, P<0.05). Univariate analyses demonstrated that the TICS had independent prognostic predictive ability for RT outcomes (P<0.05). Patients with high TICS scores showed significantly higher expression of immune-related genes. Functional pathway analyses further showed that the TICS was significantly related to immune-related biological process. Stratified analyses supported integrating TICS and tumor mutation burden (TMB) into individualized treatment planning, as an adjunct to classification by clinical stage and human papillomavirus (HPV) infection. Conclusions: The TICS model supports a personalized medicine approach to RT for HNSCC. Increased prevalence of TIIC within the tumor microenvironment (TME) confers a better prognosis for patients undergoing treatment for HNSCC.

Transcriptome Analysis Revealed Potential Neuro-Immune Interaction in Papillary Thyroid Carcinoma Tissues.

BACKGROUND: A recent study reported that papillary thyroid carcinoma (PTC) was associated with increased adrenergic nerve density. Meanwhile, emerging evidence suggested that tumor-innervating nerves might play a role in shaping the tumor microenvironment. We aimed to explore the potential interaction between neuronal markers and tumor microenvironmental signatures through a transcriptomic approach. METHODS: mRNA sequencing was conducted using five pairs of PTC and adjacent normal tissues. The Gene Set Variation Analysis (GSVA) was performed to calculate enrichment scores of gene sets related to tumor-infiltrating immune cells and the tumor microenvironment. The potential interaction was tested using the expression levels of a series of neuronal markers and gene set enrichment scores. RESULTS: PTC tissues were associated with increased enrichment scores of CD8 T cells, cancer-associated fibroblasts, mast cells, and checkpoint molecules. The neuronal marker for cholinergic neurons was positively correlated with CD8 T cell activation, while markers for serotonergic and dopaminergic neurons showed an inverse correlation. CONCLUSION: Distinct neuronal markers exerted different correlations with tumor microenvironmental signatures. Tumor-innervating nerves might play a role in the formation of the PTC microenvironment.

Prognosis-correlated Systems Involving Characteristic Diagnostic Gene Sets for Survival Analysis on Glioma.

As the most prevalent brain tumor, glioma is malignant with poor prognostic outcomes. As a result, it is of great importance to detect biomarkers for glioma diagnosis and prognosis. In this study, we determined grade-based characteristic gene clusters with gradual expression following grade change, including 1479 down- and 526 up-regulated genes. Combined interaction among proteins originating from these genes was analyzed, and hub genes were exhibited after GSEA enrichment, containing 12 and 11 genes which were correlated with prognostic outcomes, named as unfavorable and favorable gene sets, respectively. The GSVA score of each gene set was calculated and divided into high/low groups; we observed that cases in the low score group had better outcomes than the high score group based on the GSVA of the unfavorable set, while with favorable GSVA score, the low group had poorer outcomes than the high group. Eventually, we compared a variety of infiltrating immune cells between low/high GSVA subgroup, showing various immune cell types (B cell naive, activated mast cells, resting CD4 memory T cell, and so on) with opposite proportion. And interestingly, these cell types also accounted for a contrary percentage between unfavorable and favorable conditions. In conclusion, these two hub gene sets are of good importance as an evaluation system for clinical grade classification and prognosis prediction.

Exploration and Validation of Potential Biomarkers and Therapeutic Targets in Ferroptosis of Asthma.

Purpose: Asthma is a chronic inflammatory airway disease involving multiple mechanisms, of which ferroptosis is a form of programmed cell death. Recent studies have shown that ferroptosis may play a crucial role in the pathogenesis of asthma, but no specific ferroptosis gene has been found in asthma, and the exact mechanism is still unclear. The present study aimed to screen ferroptosis genes associated with asthma and find therapeutic targets, in order to contribute a new clue for the diagnosis and therapy of asthma. Methods: Ferroptosis-related differentially expressed genes (FR-DEGs) in asthma were selected by the GSE41861, GSE43696 and ferroptosis datasets. Next, the FR-DEGs were subjected by GO and KEGG enrichment, and the mRNA-miRNA network was constructed. Then, GSEA and GSVA enrichment analysis and Immune infiltration analysis were performed, followed by targeted drug prediction. Finally, the expression of FR-DEGs was confirmed using GSE63142 dataset and RT-PCR assay. Results: We found 13 FR-DEGs by the GSE41861, GSE43696 and ferroptosis database. Functional enrichment analysis revealed that the 13 FR-DEGs were enriched in oxidative stress, immune response, ferroptosis, lysosome, necrosis, apoptosis etc. Moreover, our results revealed the mRNA-miRNA network of the FR-DEGs and identified candidate drugs. Also, immune infiltration revealed that ELAVL1, CREB5, CBR1 and NR1D2 are associated with the immune cells and may be potential targets in asthma. Finally, 10 FR-DEGs were validated by the GSE63142 database. It was verified that 7 FR-DEGs were differentially expressed by collecting asthma patients and healthy controls. Conclusion: This study ultimately identified 7 FR-DEGs for the diagnosis and therapy of asthma. These 7 FR-DEGs contribute to oxidative stress and immune responses. This study provides potential therapeutic targets and biomarkers for asthma patients, shedding further light on the pathogenesis of asthma as well as providing new insights into the treatment of asthma.

Bioinformatic analysis reveals lysosome-related biomarkers and molecular subtypes in preeclampsia: novel insights into the pathogenesis of preeclampsia.

Background: The process of lysosomal biogenesis and exocytosis in preeclamptic placentae plays a role in causing maternal endothelial dysfunction. However, the specific lysosome-associated markers relevant to preeclampsia (PE) are not well-defined. Our objective is to discover new biomarkers and molecular subtypes associated with lysosomes that could improve the diagnosis and treatment of PE. Methods: We obtained four microarray datasets related to PE from the Gene Expression Omnibus (GEO) database. The limma package was utilized to identify genes that were differentially expressed between individuals with the disease and healthy controls. The logistic regression analysis was used to identify core diagnostic biomarkers, which were subsequently validated by independent datasets and clinical samples. Additionally, a consensus clustering method was utilized to distinguish between different subtypes of PE. Following this, functional enrichment analysis, GSEA, GSVA, and immune cell infiltration were conducted to compare the two subtypes and identify any differences in their functional characteristics and immune cell composition. Results: We identified 16 PE-specific lysosome-related genes. Through regression analysis, two genes, GNPTG and CTSC, were identified and subsequently validated in the external validation cohort GSE60438 and through qRT-PCR experiment. A nomogram model for the diagnosis of PE was developed and evaluated using these two genes. The model had a remarkably high predictive power (AUC values of the training set, validation set, and clinical samples were 0.897, 0.788, and 0.979, respectively). Additionally, two different molecular subtypes (C1 and C2) were identified, and we found notable variations in the levels of immune cells present in the two subtypes. Conclusion: Our results not only offered a classification system but also identified novel diagnostic biomarkers for PE patients. Our findings offered an additional understanding of how to categorize PE patients and also highlighted potential avenues for creating treatments for individuals with PE.

Accelerated glycolysis in tumor microenvironment is associated with worse survival in triple-negative but not consistently with ER+/HER2- breast cancer.

Metabolic reprogramming to sustain immortality is a hallmark of cancer and glycolysis is an important way to attain this. Thus, we investigate the association of glycolysis and associated pathways in the survival of breast cancer. A total of 5,176 breast cancer patients from multiple independent cohorts were analyzed. We determined the glycolytic signaling score by the degree of enrichment by Gene Set Variant Analysis and the median was used to divide each cohort into high vs low score groups. Glycolysis high breast cancer significantly enriched the hallmark cell proliferation-related gene sets (E2F targets, G2M checkpoint, and MYC targets v1 and v2) and was associated with high MKI67 expression. In all cohorts, triple-negative breast cancer (TNBC) was associated with the highest glycolysis score. It was found that in TNBC, glycolysis high breast cancer was associated with worse survival but in ER-positive/HER2-negative breast cancer this was not observed consistently. The glycolysis high TNBC enriched multiple pro-cancerous gene sets and was infiltrated with a low level of B-cells and anti-cancerous immune cells, and significantly associated with a decreased level of cytolytic activity. It was also observed that the glycolysis was higher in the metastatic sites than in the primary breast cancer and the survival was not affected by the metastatic sites. In conclusion, accelerated glycolysis is associated with cancer cell proliferation and worse survival in TNBC.

Comprehensive analysis reveals hub genes associated with immune cell infiltration in allergic rhinitis.

Objectives: Allergic rhinitis (AR) refers to a form of respiratory inflammation that mainly affects the sinonasal mucosa. The purpose of this study was to explore the level of immune cell infiltration and the pathogenesis of AR. Methods: We performed a comprehensive analysis of two gene expression profiles (GSE50223 and GSE50101, a total of 30 patients with AR and 31 healthy controls). CIBERSORT was used to evaluate the immune cell infiltration levels. Weighted gene coexpression network analysis was applied to explore potential genes or gene modules related to immune status, and enrichment analyses including gene ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and gene set variation analysis, were performed to analyze the potential mechanisms in AR. A protein-protein interaction network was constructed to investigate the hub genes, and consensus clustering was conducted to identify the molecular subtypes of AR. Results: Compared to the healthy controls, patients with AR had high abundance levels and proportions of CD4+ memory-activated T cells. One hundred and eight immune-related differentially expressed genes were identified. Enrichment analysis suggested that AR was mainly related to leukocyte cell-cell adhesion, cytokine-cytokine receptor interaction, T-cell activation, and T-cell receptor signaling pathway. Ten hub genes, including TYROBP, CSF1R, TLR8, FCER1G, SPI1, ITGAM, CYBB, FCGR2A, CCR1, and HCK, which were related to immune response, might be crucial to the pathogenesis of AR. Three molecular subtypes with significantly different immune statuses were identified. Conclusion: This study improves our understanding of the molecular mechanisms in AR via comprehensive strategies and provides potential diagnostic biomarkers and therapeutic targets of AR.

B cell-related tertiary lymphoid structure may exert inhibitory effects on lung adenocarcinoma and SARS-COV-2.

Background: The prognosis of lung adenocarcinoma (LUAD) is poor. Infection with coronavirus disease 2019 (COVID-19) may further worsen the outcome of LUAD. This study utilized the immune model and the COVID-19 receptor signal to identify the potential immune structure affecting the prognosis of COVID-19 and LUAD. Methods: A prognostic model was established and verified. The correlation between immune cells and risk score was examined through a variety of immune calculation methods. Gene set variation analysis (GSVA) was used to explore the correlation between the immune signaling pathway, risk model, COVID-19 binding receptor (CO19ORS) signal, and different clinicopathological factors. Results: The analysis showed that the prognosis of patients was better in the low-risk group versus the high-risk group. The tertiary lymphoid structure dominated by T and B cells (TLS1) can improve the prognosis of patients in the low-risk group. Interestingly, the CO19ORS was enriched only in females and aged >65 years. The age group >65 years is closely related to the tertiary lymphatic structure of the newborn (TLS2), while the female sex is closely related to the TLS2 and TLS1 signature. The two groups exhibited a high level of inflammation-related signal distribution. In the near future, I will collect LUAD and COVID-19 related organizations to verify the changes of 8 risk protein. Conclusion: TLS1 structure may improve the prognosis of patients with LUAD and SARS-COV-2 (Severe acute respiratory syndrome coronavirus 2). This unexpected discovery provides new insight into the comprehensive treatment of patients with LUAD and SARS-COV-2.

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer.

Background: Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods: The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the "limma" package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results: We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients' outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions: In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism-based targeted therapies for KIRC.

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy.

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Exploration of a Novel Prognostic Nomogram and Diagnostic Biomarkers Based on the Activity Variations of Hallmark Gene Sets in Hepatocellular Carcinoma.

Background: The initiation and progression of tumors were due to variations of gene sets rather than individual genes. This study aimed to identify novel biomarkers based on gene set variation analysis (GSVA) in hepatocellular carcinoma. Methods: The activities of 50 hallmark pathways were scored in three microarray datasets with paired samples with GSVA, and differential analysis was performed with the limma R package. Unsupervised clustering was conducted to determine subtypes with the ConsensusClusterPlus R package in the TCGA-LIHC (n = 329) and LIRI-JP (n = 232) cohorts. Differentially expressed genes among subtypes were identified as initial variables. Then, we used TCGA-LIHC as the training set and LIRI-JP as the validation set. A six-gene model calculating the risk scores of patients was integrated with the least absolute shrinkage and selection operator (LASSO) and stepwise regression analyses. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were performed to assess predictive performances. Multivariate Cox regression analyses were implemented to select independent prognostic factors, and a prognostic nomogram was integrated. Moreover, the diagnostic values of six genes were explored with the ROC curves and immunohistochemistry. Results: Patients could be separated into two subtypes with different prognoses in both cohorts based on the identified differential hallmark pathways. Six prognostic genes (ASF1A, CENPA, LDHA, PSMB2, SRPRB, UCK2) were included in the risk score signature, which was demonstrated to be an independent prognostic factor. A nomogram including 540 patients was further integrated and well-calibrated. ROC analyses in the five cohorts and immunohistochemistry experiments in solid tissues indicated that CENPA and UCK2 exhibited high and robust diagnostic values. Conclusions: Our study explored a promising prognostic nomogram and diagnostic biomarkers in hepatocellular carcinoma.

Elevated bile acid metabolism and microbiome are associated with suppressed cell proliferation and better survival in breast cancer.

Bile acids are metabolized by the gut microbiome and are involved in fat absorption. Contrary to their carcinogenic role in gastrointestinal cancers, bile acids have been reported to inhibit cancer cell proliferation in breast cancer. The microbiome of breast cancer tissues may also influence cancer proliferation. We hypothesized that bile acid metabolism reflects its accumulation and is associated with certain microbiomes, breast cancer biology, and patient survival. Transcriptomic and clinicopathological information of a total of 6050 patients in three large open primary breast cancer cohorts (GSE96058, METABRIC, TCGA) and 16S rRNA gene sequence microbiome data of breast cancer tissues in TCGA were analyzed by high and low bile acid metabolism scores calculated by gene set variation analysis (GSVA). Breast cancers with high bile acid metabolism had a significantly improved survival across all three cohorts. Metabolic pathways related to the production and regulation of bile acids were consistently enriched in high bile acid metabolism groups across all cohorts. On the other hand, the low bile acid metabolism group was associated with higher Ki67 expression and Nottingham histological grade, as well as enrichment of cell proliferation-related gene sets. Intratumoral heterogeneity, homologous recombination deficiency, mutational load, activation of cancer immunity, and infiltration of anticancer immune cells were also higher in this group. Gammaretrovirus, Hymenobacter, Anaerococcus, and Collimonas were significantly more abundant in the high bile acid metabolism group compared to Lactobacillus, Ruegeria, and Marichromatium in the low metabolism group. Surprisingly, almost all Hallmark cell proliferation-associated gene sets were highly enriched in all three microorganisms that were abundant in the low bile acid metabolism group. In conclusion, microorganisms abundant in the breast tumor microenvironment with low bile acid metabolism are associated with aggressive cancer biology, including increased cell proliferation and poor survival.